Land Cover Classification Using IRS LISS III Image and DEM in a Rugged Terrain: A Case Study in Himalayas

Abstract Digital image classification is generally performed to produce land cover maps from remote sensing data, particularly for large areas. The performance of image classifiers that utilize only the remote sensing data may deteriorate, especially in mountainous regions, due to the presence of shadows of high peaks. In this study, a multisource classification approach to map land cover in Himalayan region with high mountain peaks having elevations up to 4785 m above mean sea level has been adopted. Remote sensing data from IRS LISS III image along with NDVI and DEM data layers have been used to perform multi‐source classification using maximum likelihood classifier. The results show a substantial improvement in accuracy of classification on incorporation of NDVI and DEM as ancillary data over the classification performed solely on the basis of remote sensing data.

[1]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[2]  K. Valdiya.  Geology of Kumaun Lesser Himalaya , 1980 .

[3]  A. Gillespie,et al.  Remote Sensing in Geology , 1980 .

[4]  C. Justice,et al.  An examination of spectral band ratioing to reduce the topographic effect on remotely sensed data , 1981 .

[5]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[6]  T. Frank.  Mapping dominant vegetation communities in the Colorado Rocky Mountain Front Range with Landsat Thematic Mapper and digital terrain data , 1988 .

[7]  S. Ueno,et al.  Radiometric correction for atmospheric and topographic effects on Landsat MSS images , 1988 .

[8]  Paul M. Mather,et al.  Computer Processing of Remotely-Sensed Images: An Introduction , 1988 .

[9]  P. Chavez.  An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

[10]  A. Jones,et al.  Use of digital terrain data in the interpretation of SPOT-1 HRV multispectral imagery , 1988 .

[11]  D. Civco.  Topographic normalization of landsat thematic mapper digital imagery , 1989 .

[12]  J Richards,et al.  Computer processing of remotely-sensed images: An introduction , 1990 .

[13]  Peter F. Fisher,et al.  The evaluation of fuzzy membership of land cover classes in the suburban zone , 1990 .

[14]  L. Janssen,et al.  Integrating topographic data with remote sensing for land-cover classification. , 1990 .

[15]  J. Colby,et al.  Topographic Normalization in Rugged Terrain , 1991 .

[16]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[17]  N. Campbell,et al.  Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification , 1992 .

[18]  P. Gong,et al.  Integrated Analysis of Spatial Data from Multiple Sources: Using Evidential Reasoning and Artificial Neural Network Techniques for Geological Mapping , 1996 .

[19]  Giles M. Foody,et al.  Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications , 1996, Pattern Recognit. Lett..

[20]  F. Roli,et al.  Multisource Classification of Complex Rural Areas by Statistical and Neural-Network Approaches , 1997 .

[21]  A-Xing Zhu,et al.  Measuring Uncertainty in Class Assignment for Natural Resource Maps under Fuzzy Logic , 1997 .

[22]  Armando Apan,et al.  Land cover mapping for tropical forest rehabilitation planning using remotely-sensed data , 1997 .

[23]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[24]  Rajendra P. Shrestha,et al.  Application of DEM Data to Landsat Image Classification: Evaluation in a Tropical Wet-Dry Landscape of Thailand , 2000 .

[25]  D. Michelson.  Comparison of Algorithms for Classifying Swedish Landcover Using Landsat TM and ERS-1 SAR Data , 2000 .

[26]  P. Mather,et al.  Classification Methods for Remotely Sensed Data , 2001 .

[27]  Shashank Mathur,et al.  Multi-source Classification Using Artificial Neural Network in a Rugged Terrain , 2001 .

[28]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[29]  M. Arora,et al.  GIS-based Landslide Hazard Zonation in the Bhagirathi (Ganga) Valley, Himalayas , 2002 .

[30]  Manoj K. Arora,et al.  An evaluation of fuzzy classifications from IRS 1C LISS III imagery: A case study , 2003 .